14,503 research outputs found
SUPER-Net: Trustworthy Medical Image Segmentation with Uncertainty Propagation in Encoder-Decoder Networks
Deep Learning (DL) holds great promise in reshaping the healthcare industry
owing to its precision, efficiency, and objectivity. However, the brittleness
of DL models to noisy and out-of-distribution inputs is ailing their deployment
in the clinic. Most models produce point estimates without further information
about model uncertainty or confidence. This paper introduces a new Bayesian DL
framework for uncertainty quantification in segmentation neural networks:
SUPER-Net: trustworthy medical image Segmentation with Uncertainty Propagation
in Encoder-decodeR Networks. SUPER-Net analytically propagates, using Taylor
series approximations, the first two moments (mean and covariance) of the
posterior distribution of the model parameters across the nonlinear layers. In
particular, SUPER-Net simultaneously learns the mean and covariance without
expensive post-hoc Monte Carlo sampling or model ensembling. The output
consists of two simultaneous maps: the segmented image and its pixelwise
uncertainty map, which corresponds to the covariance matrix of the predictive
distribution. We conduct an extensive evaluation of SUPER-Net on medical image
segmentation of Magnetic Resonances Imaging and Computed Tomography scans under
various noisy and adversarial conditions. Our experiments on multiple benchmark
datasets demonstrate that SUPER-Net is more robust to noise and adversarial
attacks than state-of-the-art segmentation models. Moreover, the uncertainty
map of the proposed SUPER-Net associates low confidence (or equivalently high
uncertainty) to patches in the test input images that are corrupted with noise,
artifacts, or adversarial attacks. Perhaps more importantly, the model exhibits
the ability of self-assessment of its segmentation decisions, notably when
making erroneous predictions due to noise or adversarial examples
Pathologies of Neural Models Make Interpretations Difficult
One way to interpret neural model predictions is to highlight the most
important input features---for example, a heatmap visualization over the words
in an input sentence. In existing interpretation methods for NLP, a word's
importance is determined by either input perturbation---measuring the decrease
in model confidence when that word is removed---or by the gradient with respect
to that word. To understand the limitations of these methods, we use input
reduction, which iteratively removes the least important word from the input.
This exposes pathological behaviors of neural models: the remaining words
appear nonsensical to humans and are not the ones determined as important by
interpretation methods. As we confirm with human experiments, the reduced
examples lack information to support the prediction of any label, but models
still make the same predictions with high confidence. To explain these
counterintuitive results, we draw connections to adversarial examples and
confidence calibration: pathological behaviors reveal difficulties in
interpreting neural models trained with maximum likelihood. To mitigate their
deficiencies, we fine-tune the models by encouraging high entropy outputs on
reduced examples. Fine-tuned models become more interpretable under input
reduction without accuracy loss on regular examples.Comment: EMNLP 2018 camera read
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